1. Field of the Invention
Methods and apparatuses consistent with exemplary embodiments relate to video segmentation, and more particularly, to a video segmentation apparatus for extracting a desired object from a video or from images of the video, using adaptive local windows, and a method for controlling the same.
2. Description of the Related Art
Video segmentation refers to a technology for classifying pixels, which are components of a video, and to finding boundaries of an object in the video to extract the object. For example, in order to find a shape and a size of a target object in the video, the video needs to be segmented into two regions: an object region and a background region. Objects play an important role in video analysis and representation. In particular, the Moving Picture Experts Group 4 (MPEG-4) Visual standard encodes videos on an object-by-object basis. These technologies are called object-based coding technologies. The object-based coding technology may reproduce or efficiently compress a video, using a variety of object editing techniques for combining, removing and transforming objects as needed.
An example of a video segmentation-based application is a weather forecast application which provides weather information by using a virtual screen like a weather chart as a background. Another example of a video segmentation application is a virtual studio which shows video of processed combined objects on different background screens. The key technology enabling these applications is the video segmentation technology that extracts only the desired object and combines it with a different background screen.
However, computer-assisted video segmentation processing is a difficult engineering problem because videos do not have clear boundaries and every video has different characteristics. The technology, which has been developed recently separates homogenous regions having similar characteristic values in the video based on image characteristics, such as luminance values, edge information and geometric information, combines regions having similar characteristics, and masks the video using all the combined regions.
Examples of the video segmentation algorithms developed up to the present may use a boundary processing technique, a region expansion technique, a segment integration technique, a watershed technique, and an edge-based technique. Each of these techniques has their own characteristics and applications.
However, the region expansion technique or the segment integration technique require high computational complexity and repetition to find out regions having the same characteristics by searching the entire video, and, these techniques have difficulties in extracting an object with the desired precision.
The watershed technique, an improved form of the region expansion technique, is a technique for gradually approaching the desired precision by repeating a process of simultaneously dividing a video into many regions and by re-combining these regions, using a plurality of seeds. However, the watershed technique also requires high computational complexity for the desired precision of the video segmentation, and may suffer from a fragmentation problem at the boundary of the video.
In summary, the video segmentation technologies developed up to now require high computational complexity to extract an object. In addition, these technologies suffer from fragmentation., i.e., a part of the object may be truncated or a part of an object may be included in another object unintentionally, and a contiguous boundary may not be found, making it difficult to accurately extract an object.